Automatic Generation of Personalized Comment Based on User Profile
This addresses the problem of diverse user expression habits in social media comments, though it is incremental as it builds on existing NLG methods.
The paper tackles the challenge of generating personalized comments on social media by proposing a model that incorporates user profiles, resulting in natural and human-like comments based on real data from tens of thousands of users.
Comments on social media are very diverse, in terms of content, style and vocabulary, which make generating comments much more challenging than other existing natural language generation~(NLG) tasks. Besides, since different user has different expression habits, it is necessary to take the user's profile into consideration when generating comments. In this paper, we introduce the task of automatic generation of personalized comment~(AGPC) for social media. Based on tens of thousands of users' real comments and corresponding user profiles on weibo, we propose Personalized Comment Generation Network~(PCGN) for AGPC. The model utilizes user feature embedding with a gated memory and attends to user description to model personality of users. In addition, external user representation is taken into consideration during the decoding to enhance the comments generation. Experimental results show that our model can generate natural, human-like and personalized comments.